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Lookup NU author(s): Dr Olga TarasyukORCiD, Dr Anatoliy Gorbenko, Dr Jingjing ZhangORCiD, Professor Rishad ShafikORCiD, Professor Alex YakovlevORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The analysis of complex biomedical datasets is becoming central to understanding disease mechanisms, aiding risk stratification and guiding patient management. However, the utility of computational methods is often constrained by their lack of interpretability and accessibility for non-experts, which is particularly relevant in clinically critical areas where rapid initiation of targeted therapies is key. To define diagnostically relevant immune signatures in peritoneal dialysis patients presenting with acute peritonitis, we analysed a comprehensive array of cellular and soluble parameters in cloudy peritoneal effluents. Utilising Tsetlin Machines (TMs), a logic-based machine learning approach, we identified pathogen-specific immune fingerprints for different bacterial groups, each characterised by unique biomarker combinations. Unlike traditional ‘black box’ machine learning models such as artificial neural networks, TMs identified clear, logical rules in the dataset that pointed towards distinctly nuanced immune responses to different types of bacterial infection. This demonstrates unambiguously that even when infecting the same anatomical location and causing clinically indistinguishable symptoms, each type of pathogens interacts in a specific way with the body’s immune system. Importantly, these immune signatures could be easily visualised to facilitate their interpretation, thereby not only enhancing diagnostic accuracy but also potentially allowing for rapid, accurate and transparent decision-making based on the patient’s immune profile. This unique diagnostic capacity of TMs could help deliver clear and actionable insights such as early patient risk stratification and support early and informed treatment choices in advance of conventional microbiological culture results, thus guiding antibiotic stewardship and contributing to improved patient outcomes.
Author(s): Tarasyuk O, Gorbenko A, Eberl M, Topley N, Zhang J, Shafik R, Yakovlev A
Publication type: Article
Publication status: Published
Journal: Bioinformatics Advances
Year: 2025
Volume: 5
Issue: 1
Online publication date: 19/06/2025
Acceptance date: 09/06/2025
Date deposited: 28/05/2025
ISSN (electronic): 2635-0041
Publisher: Oxford University Press
URL: https://doi.org/10.1093/bioadv/vbaf140
DOI: 10.1093/bioadv/vbaf140
Data Access Statement: All underlying tools and the anonymized data underpinning this publication are available at https://github. com/anatoliy-gorbenko/biomarkers-visualization.
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